Collaborative Research: Understanding Climate Change: A Data Driven Approach

合作研究：了解气候变化：数据驱动的方法

• 批准号: 1029166
• 负责人: Alok Choudhary
• 金额: $ 90万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1029166&HistoricalAwards=false
• 关键词: Collaborative; Research; Understanding; Climate; Change

Understanding Climate Change: A Data Driven ApproachClimate change is the defining environmental challenge now facing our planet. Whether it is an increase in the frequency or intensity of hurricanes, rising sea levels, droughts, floods, or extreme temperatures and severe weather, the social, economic, and environmental consequences are great as the resource-stressed planet nears 7 billion inhabitants later this century. Yet there is considerable uncertainty as to the social and environmental impacts because the predictive potential of numerical models of the earth system is limited. These models are incapable of addressing important questions relating to food security, water resources, biodiversity, mortality, and other socio-economic issues over relevant time and spatial scales.Climate model development has contributed small and incremental improvements; however, extensive modeling gains have not been forthcoming. Modeling limitations have hampered efforts at providing information on climate change impacts and adaptation and mitigation strategies. A new and transformative approach is required to improve prediction of the potential impacts on human welfare. Data driven methods that have been highly successful in other facets of the computational sciences are now being used in the environmental sciences with success. This Expedition project will significantly advance key challenges in climate change science developing exciting and innovative new data driven approaches that take advantage of the wealth of climate and ecosystem data now available from satellite and ground-based sensors, the observational record for atmospheric, oceanic, and terrestrial processes, and physics-based climate model simulations.To realize this ambitious goal, novel methodologies appropriate to climate change science will be developed in four broad areas of data-intensive computer science: relationship mining, complex networks, predictive modeling, and high performance computing. Analysis and discovery approaches will be cognizant of climate and ecosystem data characteristics, such as non-stationarity, nonlinear processes, multi-scale nature, low-frequency variability, long-range spatial dependence, and long-memory temporal processes such as teleconnections. These innovative new approaches will be used to better understand the complex nature of the earth system and the mechanisms contributing to such climate change phenomena as hurricane frequency and intensity in the tropical Atlantic, precipitation regime shifts in the ecologically sensitive African Sahel or the Southern Great Plains, and the propensity for extreme weather events that weaken our infrastructure and result in environmental disasters with economic losses in excess of $100 billion per year in the U.S. alone.Assessments of climate change impacts, which are useful for stakeholders and policymakers, depend critically on regional and decadal scale projections of climate extremes. Thus, climate scientists often need to develop qualitative inferences about inadequately predicted climate extremes based on insights from observations (e.g., increase in hurricane intensity) or conceptual understanding (e.g., relation of wildfires to regional warming or drying and hurricanes to sea surface temperatures). These urgent societal priorities offer fertile grounds for knowledge discovery approaches. In particular, qualitative inferences on climate extremes and impacts may be transformed into quantitative predictive insights based on a combination of hypothesis-guided data analysis and relatively hypothesis-free, yet data-guided discovery processes.A primary focus of this Expedition project will be on uncertainty reduction, which can bring the complementary or supplementary skills of physics-based models together with data-guided insights regarding complex climate processes. The systematic evaluation of climate models and their component processes, as well as uncertainty assessments at regional and decadal scales is a fundamental problem that will be addressed. The ability to translate gains in the predictive skills of climate variables to improvements in impact assessments and attributions is a critical requirement for informing policymakers. Novel methodologies will be developed to gain actionable insights from disparate impacts-related datasets as well as for causal attribution or root-cause analysis. This research will be conducted in close collaboration with the climate science community and will complement insights obtained from physics-based climate models. Improved understanding of salient atmospheric processes will be provided to those contributing to the development and improvement of climate models with the goal of improving predictability. The approaches and formalisms developed in this research are expected to be applicable to a broad range of scientific and engineering problems, which use model simulations to analyze physical processes. This project will also contribute to efforts in education, diversity, community engagement, and dissemination of tools and computer and atmospheric science findings.

理解气候变化：数据驱动的方法气候变化是我们地球现在面临的决定性环境挑战。无论是飓风频率或强度的增加、海平面上升、干旱、洪水，还是极端气温和恶劣天气，随着资源紧张的地球在本世纪晚些时候接近70亿居民，其社会、经济和环境后果都是巨大的。然而，由于地球系统数值模型的预测潜力有限，因此社会和环境影响存在相当大的不确定性。这些模型无法解决与粮食安全、水资源、生物多样性、死亡率以及相关时间和空间尺度上的其他社会经济问题有关的重要问题。气候模型的开发已经做出了微小的渐进式改进;然而，广泛的建模收益尚未到来。建模的局限性阻碍了提供关于气候变化影响以及适应和缓解战略的信息的努力。需要采取一种新的变革性方法，以更好地预测对人类福祉的潜在影响。在计算科学的其他方面非常成功的数据驱动方法现在正成功地用于环境科学。该远征项目将大大推进气候变化科学的关键挑战，开发令人兴奋和创新的新数据驱动方法，利用卫星和地面传感器提供的丰富的气候和生态系统数据，大气，海洋和陆地过程的观测记录，以及基于物理的气候模型模拟。为了实现这一雄心勃勃的目标，将在数据密集型计算机科学的四个广泛领域开发适合气候变化科学的新方法：关系挖掘、复杂网络、预测建模和高性能计算。 分析和发现方法将认识到气候和生态系统数据的特点，如非平稳性、非线性过程、多尺度性质、低频变率、长距离空间依赖性和遥相关等长记忆时间过程。这些创新的新方法将用于更好地了解地球系统的复杂性质和造成气候变化现象的机制，如热带大西洋的飓风频率和强度，生态敏感的非洲萨赫勒或南部大平原的降水状况变化，以及极端天气事件的倾向，这些事件削弱了我们的基础设施，导致环境灾难，经济损失超过10亿美元。对气候变化影响的评估对利益相关者和政策制定者都很有用，但它严重依赖于对气候极端事件的区域和十年尺度预测。因此，气候科学家经常需要根据观测结果（例如，飓风强度的增加）或概念上的理解（例如，野火与区域变暖或干燥的关系以及飓风与海面温度的关系）。这些紧迫的社会优先事项为知识发现方法提供了肥沃的土壤。特别是，基于假设指导的数据分析和相对无假设但数据指导的发现过程的组合，对气候极端和影响的定性推断可以转化为定量预测见解。这可以将基于物理学的模型的补充或补充技能与关于复杂气候过程的数据指导见解结合起来。气候模式及其组成过程的系统评价以及区域和十年尺度的不确定性评估是将要解决的一个基本问题。将气候变量预测技能方面的收获转化为影响评估和归因方面的改进的能力，是向决策者提供信息的一个关键要求。将开发新的方法，从不同的影响相关数据集中获得可操作的见解，并进行因果归因或根本原因分析。这项研究将与气候科学界密切合作进行，并将补充从基于物理学的气候模型中获得的见解。 将向那些为开发和改进气候模型作出贡献的人提供对突出大气过程的更好的理解，以提高可预测性。在这项研究中开发的方法和形式主义，预计将适用于广泛的科学和工程问题，使用模型模拟来分析物理过程。 该项目还将促进教育、多样性、社区参与以及传播工具和计算机及大气科学成果方面的努力。